3D scanners provide hundreds of thousands of data points from the inspected part in a short period of time (Bi & Wang, 2010). Nowadays, blade manufacturers and the MRO industry mostly prefer using optical 3D scanners, since it is a much faster way of acquiring inspection data. The CMM data acquisition is though relatively slow and needs complicated inspection planning (Li & Gu, 2004). Traditionally, contact probes on a coordinate measuring machine (CMM) were used for data acquisition. Inspection data points are thus acquired for pre-specified sections during the data acquisition phase of inspection. The blade tolerances are typically specified and evaluated in sections (Hsu et al., 2006 Khameneifar & Feng, 2016). Accurate blade inspection is equally crucial for the MRO application for finding an effective strategy for the remanufacturing of repairable blades (Denkena et al., 2019). In addition to being used as a means of acceptance or rejection of the inspected part, the correct representation of geometric error distribution on the blade also provides the fundamental feedback for improving the associated manufacturing process. Therefore, in-service blades must also be accurately inspected during the maintenance, repair, and overhaul (MRO) operations to check for their conformance to the specified tolerances. Any deviation of the airfoil profile from its design specifications can adversely affect the performance and efficiency of the aero-engine. In addition, in-service blades are susceptible to geometric deformations due to operation in harsh environments. Once manufactured, the blades must be precisely inspected to verify their conformance to the specified tolerances (Hsu et al., 2006). These blades need to be manufactured under extremely tight tolerances. Several case studies demonstrate the effectiveness and limitations of the method.Īero-engine blades are designed for efficient energy conversion and operation in intense conditions. The thinned dataset is ordered through a profile polygon generation algorithm that can automatically modify the imperfect nodes and remove the redundant points. The sectional point set is thinned by the projection of the points onto the local curves fitted within the measurement uncertainty constraint of the data points.Ī weight function is presented that eliminates the effects of outliers on the local curve fitting. The proposed automated thinning and ordering approach enables the automatic reconstruction of airfoil profiles from unorganized sectional data points of 3D scanned blades. In addition to blade inspection, other applications such as repair and adaptive machining of aero-engine blades can equally benefit from the proposed method for automatic airfoil profile reconstruction. Implementation results have demonstrated that the proposed method is accurate and robust to noise.
#Airfoil profile series#
A series of case studies have been carried out to demonstrate the effectiveness of the proposed airfoil profile reconstruction method. Finally, a closed nonperiodic B-spline curve is fitted to the thinned and ordered set of data points to construct the smooth airfoil profile. Then, to order the thinned set of data points, the profile polygon is generated and imperfect nodes are modified by evaluation of the angular deviation of edges. For this purpose, a recursive weighted local least-squares scheme is proposed to fit local curves within the measurement uncertainty constraint of inspection data. First, the algorithm thins the scattered set of sectional data points by projecting them onto the local curves fitted to them. A three-step airfoil profile reconstruction approach is presented. This paper presents a new method to automatically reconstruct the airfoil profile from unorganized noisy sectional data points of 3D scanned blades. To maintain the accuracy of geometric error evaluation, in particular, for the position and orientation errors of the airfoil sections, sectional airfoil profiles should be reconstructed from the inspection data points. Airfoil blades are typically inspected in sections to verify their conformance to the geometric tolerances specified on their nominal design.